Conventionally, aerial imagery was captured with manned vehicles such as airplanes and helicopters or with unmanned devices such as balloons. With respect to airplanes, the cost of image acquisition was very high as the maintenance and operation of an aircraft is typically very high. While balloons offered a much lower cost, balloons have very limited maneuverability and are very much dependent on weather conditions. Indeed, some industries, such as oil and gas, must meet Federal regulatory requirements for monitoring pipelines and have, up and until recently been forced to maintain expensive acquisition systems. However, this paradigm is changing.
With the rapid development of Unmanned Aerial Vehicle (UAV) or drone technologies in recent years, the cost of acquisition has significantly lowered while maneuverability has remained very high. Thus, unlike airplanes, maintaining and operating UAVs is relatively inexpensive. In addition, unlike balloons, UAVs are highly maneuverable and less reliant on weather conditions. The advent of UAV appears to be a boon to industries requiring aerial imagery. Unfortunately, commensurate with the low cost of acquisition is that unprecedented amount of image data and numerical data is being captured. Industry estimation suggests that this trend will continue exponentially so the amount of acquired data is staggering.
In order to realize the gains in economic efficiency and the commensurate data increase, processing aerial image data to render usable data is critical. Conventionally, aerial image data may have been manually analyzed. However, manual methods are tedious and cannot keep pace with the volume of data being acquired. There are some algorithms that may sharpen or refine images, but these algorithms are often applied haphazardly by a user not conversant with the subtleties and limitations of these tools.
As such, aerial image processing is presented herein.